`cox.mode.Rd`

This function computes posterior mode estimates of the parameters of a flexible Cox model
with structured additive predictors using a Newton-Raphson algorithm. Integrals are solved
numerically. Moreover, optimum smoothing variances are computed using a stepwise optimization,
see also the details section of function `bfit`

.

```
opt_Cox(x, y, start, weights, offset,
criterion = c("AICc", "BIC", "AIC"),
nu = 0.1, update.nu = TRUE,
eps = .Machine$double.eps^0.25, maxit = 400,
verbose = TRUE, digits = 4, ...)
cox_mode(x, y, start, weights, offset,
criterion = c("AICc", "BIC", "AIC"),
nu = 0.1, update.nu = TRUE,
eps = .Machine$double.eps^0.25, maxit = 400,
verbose = TRUE, digits = 4, ...)
```

- x
The

`x`

list, as returned from function`bamlss.frame`

and transformed by function`surv_transform`

, holding all model matrices and other information that is used for fitting the model.- y
The model response, as returned from function

`bamlss.frame`

.- start
A named numeric vector containing possible starting values, the names are based on function

`parameters`

.- weights
Prior weights on the data, as returned from function

`bamlss.frame`

.- offset
Can be used to supply model offsets for use in fitting, returned from function

`bamlss.frame`

.- criterion
Set the information criterion that should be used, e.g., for smoothing variance selection. Options are the corrected AIC

`"AICc"`

, the`"BIC"`

and`"AIC"`

.- nu
Calibrates the step length of parameter updates of one Newton-Raphson update.

- update.nu
Should the updating step length be optimized in each iteration of the backfitting algorithm.

- eps
The relative convergence tolerance of the backfitting algorithm.

- maxit
The maximum number of iterations for the backfitting algorithm

- verbose
Print information during runtime of the algorithm.

- digits
Set the digits for printing when

`verbose = TRUE`

.- ...
Currently not used.

A list containing the following objects:

- fitted.values
A named list of the fitted values of the modeled parameters of the selected distribution.

- parameters
The estimated set regression coefficients and smoothing variances.

- edf
The equivalent degrees of freedom used to fit the model.

- logLik
The value of the log-likelihood.

- logPost
The value of the log-posterior.

- hessian
The Hessian matrix evaluated at the posterior mode.

- converged
Logical, indicating convergence of the backfitting algorithm.

- time
The runtime of the algorithm.

Umlauf N, Klein N, Zeileis A (2016). Bayesian Additive Models for Location
Scale and Shape (and Beyond). *(to appear)*

```
## Please see the examples of function sam_Cox()!
```